U.S. patent application number 10/177824 was filed with the patent office on 2003-12-25 for systems and methods for generating prediction queries.
Invention is credited to Guan, Rong Jian, Netz, Amir M., Oveson, Scott Conrad, Tang, Zhaohui.
Application Number | 20030236784 10/177824 |
Document ID | / |
Family ID | 29734506 |
Filed Date | 2003-12-25 |
United States Patent
Application |
20030236784 |
Kind Code |
A1 |
Tang, Zhaohui ; et
al. |
December 25, 2003 |
Systems and methods for generating prediction queries
Abstract
Systems and methods are provided for generating prediction
queries to help a user build and execute prediction queries. A user
interface (UI) is provided that is easy to use and understand in
connection with the generation of a prediction query for data
mining. The UI can be instantiated from a variety of disparate
sources that may request query building services. While prediction
queries and relational queries are quite different, the UI enables
prediction queries to be built in a manner that is similar to the
way relational queries are built. In one embodiment, the main
screen of the UI includes four main components: (1) a table column
mapping area, (3) a selection grid area, (4) a query text display
area and (5) a query result grid area. In one embodiment, the query
text display area and the query result grid area are initially not
presented to the user.
Inventors: |
Tang, Zhaohui; (Bellevue,
WA) ; Guan, Rong Jian; (Sammamish, WA) ; Netz,
Amir M.; (Bellevue, WA) ; Oveson, Scott Conrad;
(Sammamish, WA) |
Correspondence
Address: |
WOODCOCK WASHBURN LLP
ONE LIBERTY PLACE, 46TH FLOOR
1650 MARKET STREET
PHILADELPHIA
PA
19103
US
|
Family ID: |
29734506 |
Appl. No.: |
10/177824 |
Filed: |
June 21, 2002 |
Current U.S.
Class: |
1/1 ;
707/999.006 |
Current CPC
Class: |
G06F 16/2428 20190101;
Y10S 707/99932 20130101; Y10S 707/99942 20130101; G06F 2216/03
20130101; Y10S 707/99943 20130101; Y10S 707/99933 20130101 |
Class at
Publication: |
707/6 |
International
Class: |
G06F 017/30 |
Claims
What is claimed is:
1. A method for generating a prediction query, comprising:
selecting at least one mining model; selecting at least one input
table; joining at least one element of the at least one mining
model with at least one element of the at least one input table;
and generating a prediction query in response to said joining.
2. A method according to claim 1, further comprising: joining at
least one column of the a first input table with at least one
column of second input table.
3. A method according to claim 1, further comprising: executing
said prediction query thereby producing at least one prediction
result data set.
4. A method according to claim 3, further comprising: displaying
the at least one prediction result data set in a grid view.
5. A method according to claim 1, further comprising: displaying at
least one element of said at least one mining model; and displaying
at least one element of said at least one input table.
6. A method according to claim 5, further including displaying said
at least one mining model as a relational table.
7. A method according to claim 1, wherein said at least one input
table is at least one relational table.
8. A method according to claim 1, wherein said joining includes
specifying a line segment to mark a join condition.
9. A method according to claim 1, further comprising: displaying a
table column mapping area and a selection grid area.
10. A method according to claim 9, wherein displaying the table
column mapping area includes at least one of (A) displaying said at
least one mining model with at least one mining model column and
(B) displaying at least one schema of at least one relational table
of said at least one input table having at least one input table
column.
11. A method according to claim 10, further comprising dragging and
dropping to said selection grid area at least one column from at
least one of (A) a mining model of the table column mapping area
and (B) an input table of the table column mapping area.
12. A method according to claim 11, wherein if there are nested
tables, further comprising inputting a mapping between a case table
and a corresponding nested table.
13. A method according to claim 11, further comprising selecting at
least one available prediction function in said selection grid
area.
14. A method according to claim 9, wherein displaying the table
column mapping area includes displaying a nested table link
enabling a user to link a table having specific hierarchical and
nested relationships to said at least one input table.
15. A method according to claim 9, where said selection grid area
includes at least one of a source column, a field column, an alias
column, a show column, a group column, an And/Or column, and a
criteria column, wherein the source column allows a user to select
at least one column from at least one of (A) at least one mining
model, (B) the at least one input table, (C) at least one available
prediction function and (D) at least one user expression, wherein
the field column allows a user to pick at least one column and at
least one prediction function, where n the alias column allows a
user to rename the display name of a column in the result grid
view, wherein the show column allows a user to show a source in the
grid area, wherein the group column allows the user to group at
least two boolean expressions together, wherein the And/Or column
allows a user to specify a boolean expression and wherein the user
can type the condition or user expression in the criteria
column.
16. A method according to claim 1, wherein said joining includes
linking at least one column of said at least one mining model and
to at least one column of said at least one input table by
inputting at least one line segment therebetween.
17. A method according to claim 1, further comprising: displaying
query syntax of the generated prediction query.
18. A method according to claim 1, wherein said selecting at least
one mining model includes selecting one of a trained mining model
within an opened project and an existing model from a server.
19. A method according to claim 18, wherein the default selected
mining model is the first one in a list of derived mining
models.
20. A method according to claim 1, wherein said selection of at
least one input table includes selecting one of a table from
existing Data Source Views and a table from a server.
21. A method according to claim 1, further comprising:
automatically displaying a mapping between an element from a model
of said at least one mining model and an element from a table of
said at least one input table, when the elements have same
name.
22. A method according to claim 1, further comprising: displaying a
toolbar, wherein said toolbar includes at least one of a (A) a save
component, which saves a prediction query, (B) an open component,
which opens a query, (C) a run component, which executes a
prediction query, (D) a show syntax component, which displays query
syntax associated with a prediction query in a window and (E) a
design/grid viewer toggle component, which switches back and forth
between a design view and a query result view.
23. At least one of an operating system, driver code, an
application programming interface, a tool kit and a coprocessing
device for providing the image rendering of claim 1.
24. A modulated data signal carrying computer executable
instructions for performing the method of claim 1.
25. A computing device comprising means for performing the method
of claim 1.
26. At least one computer readable medium for generating a
prediction query having stored thereon a plurality of
computer-executable modules comprising computer executable
instructions, the modules comprising: a first selecting component
for selecting at least one mining model; a second selecting
component for selecting at least one input table; a joining
component for building a relationship between said at least one
input table by joining columns between input tables; a joining
component for joining at least one element of the at least one
mining model with at least one element of the at least one input
table; and a generating component for generating a prediction query
in response to said joining.
27. At least one computer readable medium according to claim 26,
further comprising: an executing component for executing said
prediction query thereby producing at least one prediction result
data set.
28. At least one computer readable medium according to claim 26,
wherein said at least one input table is at least one relational
table.
29. At least one computer readable medium according to claim 26,
wherein said joining component includes a specifying mechanism for
specifying a line segment to mark a join condition.
30. At least one computer readable medium according to claim 26,
further comprising: a displaying component for displaying a table
column mapping area and a selection grid area.
31. At least one computer readable medium according to claim 30,
wherein the displaying component for displaying the table column
mapping area includes at least one of (1) displaying said at least
one mining model with at least one mining model column and (3)
displaying at least one schema of at least one relational table of
said at least one input table having at least one input table
column.
32. At least one computer readable medium according to claim 30,
further comprising a dragging and dropping component for dragging
and dropping at least one column from at least one of (1) mining
model of the table column mapping area and (3) an input table of
the table column mapping area, to said selection grid area.
33. At least one computer readable medium according to claim 32,
wherein if there are nested tables, further comprising an inputting
component for inputting a mapping between a case table and a
corresponding nested table.
34. At least one computer readable medium according to claim 32,
further comprising dragging and dropping at least one available
prediction function to said selection grid area.
35. At least one computer readable medium according to claim 26,
wherein said joining component includes slinking component for
linking at least one column of said at least one mining model and
to at least one column of said at least one input table by
inputting at least one line segment therebetween.
36. At least one computer readable medium according to claim 26,
further comprising: a displaying component for displaying query
syntax of the generated prediction query.
37. At least one computer readable medium according to claim 26,
further comprising: a displaying component for automatically
displaying a mapping between an element from a model of said at
least one mining model and an element from a table of said at least
one input table, when the elements have same name.
38. A computing device for use in connection with generating a
prediction query, comprising the modules of claim 26.
39. A computing system for generating a prediction query,
comprising: means for selecting at least one mining model; means
for selecting at least one input table; means for joining at least
one element of the at least one mining model with at least one
element of the at least one input table; and means for generating a
prediction query in response to said means for joining.
40. A computing system according to claim 39, further comprising:
means for joining columns between said at least one input
table.
41. A computing system according to claim 40, further comprising:
means executing said prediction query thereby producing at least
one prediction result data set.
42. A computing system according to claim 41, further comprising:
means for displaying the at least one prediction result data set in
a grid view.
43. A computing system according to claim 40, further comprising:
means for displaying at least one element of said at least one
mining model; and means for displaying at least one element of said
at least one input table.
44. A computing system according to claim 43, further including
means for displaying said at least one mining model as a relational
table.
45. A computing system according to claim 40, wherein said at least
one input table is at least one relational table.
46. A computing system according to claim 40, wherein said means
for joining includes means for specifying a line segment to mark a
join condition.
47. A computing system according to claim 40, further comprising:
means for displaying a table column mapping area and a selection
grid area.
48. A computing system according to claim 47, wherein the means for
displaying the table column mapping area includes at least one of
(A) means for displaying said at least one mining model with at
least one mining model column and (B) means for displaying at least
one schema of at least one relational table of said at least one
input table having at least one input table column.
49. A computing system according to claim 48, further comprising
means for dragging and dropping to said selection grid area at
least one column from at least one of (A) a mining model of the
table column mapping area and (B) an input table of the table
column mapping area.
50. A computing system according to claim 49, wherein if there are
nested tables, further comprising means for inputting a mapping
between a case table and a corresponding nested table.
51. A computing system according to claim 49, further comprising
means for dragging and dropping at least one available prediction
function to said selection grid area.
52. A computing system according to claim 48, wherein the means for
displaying the table column mapping area includes means for
displaying a nested table link enabling a user to link a table
having specific hierarchical and nested relationships to said at
least one input table.
53. A computing system according to claim 48, where said selection
grid area includes at least one of a source column, a field column,
an alias column, a show column, a group column, an And/Or column,
and a criteria column, wherein the source column allows a user to
select at least one column from at least one of (A) at least one
mining model, (B) the at least one input table, (C) at least one
available prediction function and (D) at least one user expression,
wherein the field column allows a user to pick at least one column
and at least one prediction function, where in the alias column
allows a user to rename the display name of a column in the result
grid view, wherein the show column allows a user to show a source
in the grid area, wherein the group column allows the user to group
at least two boolean expressions together, wherein the And/Or
column allows a user to specify a boolean expression and wherein
the user can type the condition or user expression in the criteria
column.
54. A computing system according to claim 40, wherein said means
for joining includes means for linking at least one column of said
at least one mining model and to at least one column of said at
least one input table by inputting at least one line segment
therebetween.
55. A computing system according to claim 40, further comprising:
means for displaying query syntax of the generated prediction query
according to said means for generating.
56. A computing system according to claim 40, wherein said means
for selecting at least one mining model includes means for
selecting one of a trained mining model within an opened project
and an existing model from a server.
57. A computing system according to claim 56, wherein the default
selected mining model by said means for selecting is the first one
in a list of derived mining models.
58. A computing system according to claim 40, wherein said means
for selecting at least one input table includes selecting one of a
table from existing Data Source Views and a table from a
server.
59. A computing system according to claim 40, further comprising:
means for automatically displaying a mapping between an element
from a model of said at least one mining model and an element from
a table of said at least one input table, when the elements have
same name.
60. A computing system according to claim 40, further comprising:
means for displaying a toolbar, wherein said toolbar includes at
least one of a (A) a save component, which saves a prediction
query, (B) an open component, which opens a query, (C) a run
component, which executes a prediction query, (D) a show syntax
component, which displays query syntax associated with a prediction
query in a window and (E) a design/grid viewer toggle component,
which switches back and forth between a design view and a query
result view.
Description
FIELD OF THE INVENTION
[0001] The present invention is directed to systems and methods for
generating and executing prediction queries.
BACKGROUND OF THE INVENTION
[0002] A prediction query for data mining (DM) applies a prediction
model to transactional data, or other kinds of data, and generates
predictive results that can serve as the basis for sound business
decisions in marketing, operations, budgeting and many other areas
as well. The advantages and capabilities for data mining are
similar to those of On-Line Analytical Processing (OLAP), but break
much more ground. Like OLAP, DM exists to help one obtain
qualitative information from otherwise dry, transactional data.
While OLAP achieves this by optimizing drill-down queries and
letting users observe patterns in data, DM actively analyzes data
and determines patterns on its own. DM is based in part on
artificial intelligence (AI) principles and algorithms, and is also
based heavily on statistics. DM is relevant to a variety of
applications, including, but not limited to, client/server
applications and services, data warehousing, web site
personalization, on-line customer assessment, fraud detection,
etc.
[0003] FIG. 1 illustrates an exemplary prior art user interface for
a relational query builder 30. For instance, join operations
between relational tables 40 and 42 can be specified, and automatic
mappings 44 are created between tables 40 and 42. Grid view 50
enables a user to select, e.g., "drag and drop," columns from any
of the tables to the grid in order to build a join query in a
relational system. Relational query builder 30 thus provides a
standard way to build relational queries; however, to date, there
is no standard way to build a prediction query.
[0004] An application or object that allows prediction models to be
built using data mining algorithms is sometimes called a prediction
query builder or generator. A prediction query builder typically
can be applied to a variety of kinds and sizes of databases. In
this regard, a prediction query builder enables the incorporation
of predictive data mining models (DMM) from wherever they may be
located. A DMM is like a relational table, except that it typically
includes special columns that can be used for data training and
prediction making, i.e., the DMM enables both the creation of a
prediction model and the generation of predictions. Unlike a
standard relational table, though, which stores raw data, a DMM
stores the patterns discovered by the particular data mining
algorithm that was utilized.
[0005] A prediction join operation is an operation that is mapped
to a join query between a trained data mining model and a
designated input data source so that one can generate a tailored
prediction result. The prediction result can then be stored,
interpreted, output or displayed in a variety of formats.
[0006] Whatever the platform may be to interact with the data, in
order to access the data to be mined, a DM engine formulates a
query according to the format of the platform, e.g., SQL Server, in
which the data is stored. Regardless of the platform, describing a
prediction query in an unambiguous way can be challenging. Thus,
creating prediction queries from scratch can be a complex, tedious
and error-prone process. Among all other data mining tools
currently available in the marketplace, there is no product that
provides a simple, graphical way to build a prediction query. Thus,
there exists a need in data mining products for a tool that can
assist a user in building and executing a data mining prediction
query in a standard manner, simply and easily. There is still
further a need for a prediction query builder that allows a user to
build data mining queries in a manner similar to building/executing
relational join queries. There is thus a need for improvement over
these and other deficiencies of the prior art.
SUMMARY OF THE INVENTION
[0007] In view of the foregoing, the present invention provides
systems and methods for generating prediction queries to help a
user build and execute prediction queries. A user interface (UI) is
provided that is easy to use and understand in connection with the
generation of a prediction query for data mining, and the UI can be
instantiated from a variety of disparate sources that may request
query building services. While prediction queries and relational
queries are quite different, the UI of the invention enables
prediction queries to be built in a manner that is similar to the
way relational queries are built. In one embodiment, the main
screen of the UI includes four main components: (1) a table column
mapping area, (2) a selection grid area, (3) a query text display
area and (4) a query result grid area. In one embodiment, the query
text display area and the query result grid area are initially
invisible.
[0008] Other features and embodiments of the present invention are
described below.
BRIEF DESCRIPTION OF THE DRAWINGS
[0009] The file of this patent includes at least one drawing
executed in color. Copies of this patent with color drawings will
be provided by the United States Patent and Trademark Office upon
request and payment of the necessary fee.
[0010] The system and methods for generating prediction queries in
accordance with the present invention are further described with
reference to the accompanying drawings in which:
[0011] FIG. 1 is a prior art illustration of a UI for a relational
query builder;
[0012] FIG. 2A is a block diagram representing an exemplary network
environment having a variety of computing devices in which the
present invention may be implemented;
[0013] FIG. 2B is a block diagram representing an exemplary
non-limiting computing device in which the present invention may be
implemented;
[0014] FIGS. 3A and 3B illustrate a main screen of an exemplary
embodiment of the UI of the invention;
[0015] FIG. 4 illustrates an exemplary display of query syntax of a
prediction query in accordance with the present invention;
[0016] FIG. 5 illustrates exemplary results of an execution of a
prediction query in accordance with the invention;
[0017] FIG. 6 illustrates an exemplary table mapping column area in
accordance with the invention;
[0018] FIGS. 7A to 7C illustrate exemplary aspects of inputting
mining model(s) and input table(s) in accordance with the
invention;
[0019] FIG. 8 illustrates automatic mapping of model names and
table names in accordance with the invention;
[0020] FIGS. 9A and 9B illustrate exemplary aspects of a selection
grid area in accordance with the invention; and
[0021] FIG. 10 illustrates an exemplary generation of a prediction
query and associated syntax in accordance with the invention.
DETAILED DESCRIPTION OF THE INVENTION
[0022] Overview
[0023] As explained in the background, there exists a need in data
mining for a tool that can assist a user in building and executing
a data mining prediction query. The prediction query builder of the
present invention allows a user to build data mining queries in a
manner similar to building/executing relational join queries. In
one aspect, the data mining model is treated similarly to a
relational table and predictions are treated similarly to a join
operation. However, instead of joining two relational tables, the
present invention joins a relational table with a mining model. An
improved UI permits a user to use line segments to mark the join
condition and build the query simply and easily. In one embodiment,
the main screen of the UI includes four main components: (1) a
table column mapping area, (2) a selection grid area, (3) a query
text display area and (4) a query result grid area. In one
embodiment, the query text display area and the query result grid
area are initially not presented to the user.
[0024] Exemplary Networked and Distributed Environments
[0025] One of ordinary skill in the art can appreciate that a
computer or other client or server device can be deployed as part
of a computer network, or in a distributed computing environment.
In this regard, the present invention pertains to any computer
system having any number of memory or storage units, and any number
of applications and processes occurring across any number of
storage units or volumes, which may be used in connection with a
prediction query generation process. The present invention may
apply to an environment with server computers and client computers
deployed in a network environment or distributed computing
environment, having remote or local storage. The present invention
may also be applied to standalone computing devices, having
programming language functionality, interpretation and execution
capabilities for generating, receiving and transmitting information
in connection with remote or local prediction query generation
services.
[0026] Distributed computing facilitates sharing of computer
resources and services by direct exchange between computing devices
and systems. These resources and services include the exchange of
information, cache storage, and disk storage for files. Distributed
computing takes advantage of network connectivity, allowing clients
to leverage their collective power to benefit the entire
enterprise. In this regard, a variety of devices may have
applications, objects or resources that may implicate a prediction
query generation process that may utilize the techniques of the
present invention.
[0027] FIG. 2A provides a schematic diagram of an exemplary
networked or distributed computing environment. The distributed
computing environment comprises computing objects 10a, 10b, etc.
and computing objects or devices 110a, 110b, 110c, etc. These
objects may comprise programs, methods, data stores, programmable
logic, etc. The objects may comprise portions of the same or
different devices such as personal digital assistants (PDAs),
televisions, Moving Picture Experts Group (MPEG-1) Audio Layer-3
(MP3) players, televisions, personal computers, etc. Each object
can communicate with another object by way of the communications
network 14. This network may itself comprise other computing
objects and computing devices that provide services to the system
of FIG. 2A. In accordance with an aspect of the invention, each
object 10a, 10b, etc. or 110a, 110b, 110c, etc. may contain an
application that might request forward mapping services.
[0028] In a distributed computing architecture, computers, which
may have traditionally been used solely as clients, communicate
directly among themselves and can act as both clients and servers,
assuming whatever role is most efficient for the network. This
reduces the load on servers and allows all of the clients to access
resources available on other clients, thereby increasing the
capability and efficiency of the entire network. Prediction query
generation and execution services and interfaces in accordance with
the present invention may thus be distributed among clients and
servers, acting in a way that is efficient for the entire
network.
[0029] Distributed computing can help businesses deliver services
and capabilities more efficiently across diverse geographic
boundaries. Moreover, distributed computing can move data closer to
the point where data is consumed acting as a network caching
mechanism. Distributed computing also allows computing networks to
dynamically work together using intelligent agents. Agents reside
on peer computers and communicate various kinds of information back
and forth. Agents may also initiate tasks on behalf of other peer
systems. For instance, intelligent agents can be used to prioritize
tasks on a network, change traffic flow, search for files locally
or determine anomalous behavior such as a virus and stop it before
it affects the network. All sorts of other services may be
contemplated as well. Since data may in practice be physically
located in one or more locations, the ability to distribute
prediction query generation and execution services and interfaces
is of great utility in such a system.
[0030] It can also be appreciated that an object, such as 110c, may
be hosted on another computing device 10a, 10b, etc. or 110a, 110b,
etc. Thus, although the physical environment depicted may show the
connected devices as computers, such illustration is merely
exemplary and the physical environment may alternatively be
depicted or described comprising various digital devices such as
PDAs, televisions, MP3 players, etc., software objects such as
interfaces, COM objects and the like.
[0031] There are a variety of systems, components, and network
configurations that support distributed computing environments. For
example, computing systems may be connected together by wireline or
wireless systems, by local networks or widely distributed networks.
Currently, many of the networks are coupled to the Internet, which
provides the infrastructure for widely distributed computing and
encompasses many different networks.
[0032] In home networking environments, there are at least four
disparate network transport media that may each support a unique
protocol, such as Power line, data (both wireless and wired),
voice, e.g., telephone, and entertainment media. Most home control
devices such as light switches and appliances may use power line
for connectivity. Data Services may enter the home as broadband
(e.g., either DSL or Cable modem) and are accessible within the
home using either wireless, e.g., Home Radio Frequency (HomeRF) or
802.11b, or wired, e.g., Home Phoneline Networking Appliance (PNA),
Cat 5, even power line, connectivity. Voice traffic may enter the
home either as wired, e.g., Cat 3, or wireless, e.g., cell phones,
and may be distributed within the home using Cat 3 wiring.
Entertainment media, or other data, may enter the home either
through satellite or cable and is typically distributed in the home
using coaxial cable. IEEE 1394 and digital video interface (DVI)
are also emerging as digital interconnects for clusters of media
devices. All of these network environments and others that may
emerge as protocol standards may be interconnected to form an
intranet that may be connected to the outside world by way of the
Internet. In short, a variety of disparate sources exist for the
storage and transmission of data, and consequently, moving forward,
computing devices will require ways of sharing data, such as data
accessed or utilized incident to prediction query generation and
execution in accordance with the present invention.
[0033] The Internet commonly refers to the collection of networks
and gateways that utilize the Transport Control Protocol/Interface
Program (TCP/IP) suite of protocols, which are well-known in the
art of computer networking. The Internet can be described as a
system of geographically distributed remote computer networks
interconnected by computers executing networking protocols that
allow users to interact and share information over the networks.
Because of such wide-spread information sharing, remote networks
such as the Internet have thus far generally evolved into an open
system for which developers can design software applications for
performing specialized operations or services, essentially without
restriction.
[0034] Thus, the network infrastructure enables a host of network
topologies such as client/server, peer-to-peer, or hybrid
architectures. The "client" is a member of a class or group that
uses the services of another class or group to which it is not
related. Thus, in computing, a client is a process, i.e., roughly a
set of instructions or tasks, that requests a service provided by
another program. The client process utilizes the requested service
without having to "know" any working details about the other
program or the service itself. In a client/server architecture,
particularly a networked system, a client is usually a computer
that accesses shared network resources provided by another
computer, e.g., a server. In the example of FIG. 2A, computers
110a, 110b, etc. can be thought of as clients and computer 10a,
10b, etc. can be thought of as the server where server 10a, 10b,
etc. maintains the data that is then replicated in the client
computers 110a, 110b, etc.
[0035] A server is typically a remote computer system accessible
over a remote network such as the Internet. The client process may
be active in a first computer system, and the server process may be
active in a second computer system, communicating with one another
over a communications medium, thus providing distributed
functionality and allowing multiple clients to take advantage of
the information-gathering capabilities of the server.
[0036] Client and server communicate with one another utilizing the
functionality provided by a protocol layer. For example,
Hypertext-Transfer Protocol (HTTP) is a common protocol that is
used in conjunction with the World Wide Web (WWW). Typically, a
computer network address such as a Universal Resource Locator (URL)
or an Internet Protocol (IP) address is used to identify the server
or client computers to each other. The network address can be
referred to as a URL address. For example, communication can be
provided over a communications medium. In particular, the client
and server may be coupled to one another via TCP/IP connections for
high-capacity communication.
[0037] Thus, FIG. 2A illustrates an exemplary networked or
distributed environment, with a server in communication with client
computers via a network/bus, in which the present invention may be
employed. In more detail, a number of servers 10a, 10b, etc., are
interconnected via a communications network/bus 14, which may be a
LAN, WAN, intranet, the Internet, etc., with a number of client or
remote computing devices 110a, 110b, 110c, 110d, 110e, etc., such
as a portable computer, handheld computer, thin client, networked
appliance, or other device, such as a video cassette recorder
(VCR), television (TV), oven, light, heater and the like in
accordance with the present invention. It is thus contemplated that
the present invention may apply to any computing device in
connection with which it is desirable to process or display
prediction data.
[0038] In a network environment in which the communications
network/bus 14 is the Internet, for example, the servers 10a, 10b,
etc. can be Web servers with which the clients 110a, 110b, 110c,
110d, 110e, etc. communicate via any of a number of known protocols
such as HTTP. Servers 10a, 10b, etc. may also serve as clients
110a, 110b, 110c, 110d, 110e, etc., as may be characteristic of a
distributed computing environment. Communications may be wired or
wireless, where appropriate. Client devices 110a, 110b, 110c, 110d,
110e, etc. may or may not communicate via communications
network/bus 14, and may have independent communications associated
therewith. For example, in the case of a TV or VCR, there may or
may not be a networked aspect to the control thereof. Each client
computer 110a, 110b, 110c, 110d, 110e, etc. and server computer
10a, 10b, etc. may be equipped with various application program
modules or objects 135 and with connections or access to various
types of storage elements or objects, across which files may be
stored or to which portion(s) of files or images may be downloaded
or migrated. Any computer 10a, 10b, 110a, 110b, etc. may be
responsible for the maintenance and updating of a database 20 or
other storage element in accordance with the present invention,
such as a database or memory 20 for storing data or intermediate
object(s) processed according to the invention. Thus, the present
invention can be utilized in a computer network environment having
client computers 110a, 110b, etc. that can access and interact with
a computer network/bus 14 and server computers 10a, 10b, etc. that
may interact with client computers 110a, 110b, etc. and other like
devices, and databases 20.
[0039] Exemplary Computing Device
[0040] FIG. 2B and the following discussion are intended to provide
a brief general description of a suitable computing environment in
which the invention may be implemented. It should be understood,
however, that handheld, portable and other computing devices and
computing objects of all kinds are contemplated for use in
connection with the present invention, as described above. Thus,
while a general purpose computer is described below, this is but
one example, and the present invention may be implemented with
other computing devices, such as a thin client having network/bus
interoperability and interaction. Thus, the present invention may
be implemented in an environment of networked hosted services in
which very little or minimal client resources are implicated, e.g.,
a networked environment in which the client device serves merely as
an interface to the network/bus, such as an object placed in an
appliance, or other computing devices and objects as well. In
essence, anywhere that data may be stored or from which data may be
retrieved is a desirable, or suitable, environment for operation of
the prediction query generation and execution techniques of the
invention.
[0041] Although not required, the invention can be implemented via
an operating system, for use by a developer of services for a
device or object, and/or included within application software that
operates in connection with the prediction query generation and
execution techniques of the invention. Software may be described in
the general context of computer-executable instructions, such as
program modules, being executed by one or more computers, such as
client workstations, servers or other devices. Generally, program
modules include routines, programs, objects, components, data
structures and the like that perform particular tasks or implement
particular abstract data types. Typically, the functionality of the
program modules may be combined or distributed as desired in
various embodiments. Moreover, those skilled in the art will
appreciate that the invention may be practiced with other computer
system configurations. Other well known computing systems,
environments, and/or configurations that may be suitable for use
with the invention include, but are not limited to, personal
computers (PCs), automated teller machines, server computers,
hand-held or laptop devices, multi-processor systems,
microprocessor-based systems, programmable consumer electronics,
network PCs, appliances, lights, environmental control elements,
minicomputers, mainframe computers and the like. The invention may
also be practiced in distributed computing environments where tasks
are performed by remote processing devices that are linked through
a communications network/bus or other data transmission medium. In
a distributed computing environment, program modules may be located
in both local and remote computer storage media including memory
storage devices, and client nodes may in turn behave as server
nodes.
[0042] FIG. 2B thus illustrates an example of a suitable computing
system environment 100 in which the invention may be implemented,
although as made clear above, the computing system environment 100
is only one example of a suitable computing environment and is not
intended to suggest any limitation as to the scope of use or
functionality of the invention. Neither should the computing
environment 100 be interpreted as having any dependency or
requirement relating to any one or combination of components
illustrated in the exemplary operating environment 100.
[0043] With reference to FIG. 2B, an exemplary system for
implementing the invention includes a general purpose computing
device in the form of a computer 110. Components of computer 110
may include, but are not limited to, a processing unit 120, a
system memory 130, and a system bus 121 that couples various system
components including the system memory to the processing unit 120.
The system bus 121 may be any of several types of bus structures
including a memory bus or memory controller, a peripheral bus, and
a local bus using any of a variety of bus architectures. By way of
example, and not limitation, such architectures include Industry
Standard Architecture (ISA) bus, Micro Channel Architecture (MCA)
bus, Enhanced ISA (EISA) bus, Video Electronics Standards
Association (VESA) local bus, and Peripheral Component Interconnect
(PCI) bus (also known as Mezzanine bus).
[0044] Computer 110 typically includes a variety of computer
readable media. Computer readable media can be any available media
that can be accessed by computer 110 and includes both volatile and
nonvolatile media, removable and non-removable media. By way of
example, and not limitation, computer readable media may comprise
computer storage media and communication media. Computer storage
media includes both volatile and nonvolatile, removable and
non-removable media implemented in any method or technology for
storage of information such as computer readable instructions, data
structures, program modules or other data. Computer storage media
includes, but is not limited to, Random Access Memory (RAM), Read
Only Memory (ROM), Electrically Erasable Programmable Read Only
Memory (EEPROM), flash memory or other memory technology, Compact
Disk Read Only Memory (CDROM), digital versatile disks (DVD) or
other optical disk storage, magnetic cassettes, magnetic tape,
magnetic disk storage or other magnetic storage devices, or any
other medium which can be used to store the desired information and
which can accessed by computer 110. Communication media typically
embodies computer readable instructions, data structures, program
modules or other data in a modulated data signal such as a carrier
wave or other transport mechanism and includes any information
delivery media. The term "modulated data signal" means a signal
that has one or more of its characteristics set or changed in such
a manner as to encode information in the signal. By way of example,
and not limitation, communication media includes wired media such
as a wired network or direct-wired connection, and wireless media
such as acoustic, RF, infrared and other wireless media.
Combinations of any of the above should also be included within the
scope of computer readable media.
[0045] The system memory 130 includes computer storage media in the
form of volatile and/or nonvolatile memory such as read only memory
(ROM) 131 and random access memory (RAM) 132. A basic input/output
system 133 (BIOS), containing the basic routines that help to
transfer information between elements within computer 110, such as
during start-up, is typically stored in ROM 131. RAM 132 typically
contains data and/or program modules that are immediately
accessible to and/or presently being operated on by processing unit
120. By way of example, and not limitation, FIG. 2B illustrates
operating system 134, application programs 135, other program
modules 136, and program data 137.
[0046] The computer 110 may also include other
removable/non-removable, volatile/nonvolatile computer storage
media. By way of example only, FIG. 2B illustrates a hard disk
drive 141 that reads from or writes to non-removable, nonvolatile
magnetic media, a magnetic disk drive 151 that reads from or writes
to a removable, nonvolatile magnetic disk 152, and an optical disk
drive 155 that reads from or writes to a removable, nonvolatile
optical disk 156, such as a CD ROM or other optical media. Other
removable/non-removable, volatile/nonvolatile computer storage
media that can be used in the exemplary operating environment
include, but are not limited to, magnetic tape cassettes, flash
memory cards, digital versatile disks, digital video tape, solid
state RAM, solid state ROM, and the like. The hard disk drive 141
is typically connected to the system bus 121 through an
non-removable memory interface such as interface 140, and magnetic
disk drive 151 and optical disk drive 155 are typically connected
to the system bus 121 by a removable memory interface, such as
interface 150.
[0047] The drives and their associated computer storage media
discussed above and illustrated in FIG. 2B provide storage of
computer readable instructions, data structures, program modules
and other data for the computer 110. In FIG. 2B, for example, hard
disk drive 141 is illustrated as storing operating system 144,
application programs 145, other program modules 146, and program
data 147. Note that these components can either be the same as or
different from operating system 134, application programs 135,
other program modules 136, and program data 137. Operating system
144, application programs 145, other program modules 146, and
program data 147 are given different numbers here to illustrate
that, at a minimum, they are different copies. A user may enter
commands and information into the computer 110 through input
devices such as a keyboard 162 and pointing device 161, commonly
referred to as a mouse, trackball or touch pad. Other input devices
(not shown) may include a microphone, joystick, game pad, satellite
dish, scanner, or the like. These and other input devices are often
connected to the processing unit 120 through a user input interface
160 that is coupled to the system bus 121, but may be connected by
other interface and bus structures, such as a parallel port, game
port or a universal serial bus (USB). A graphics interface 182,
such as Northbridge, may also be connected to the system bus 121.
Northbridge is a chipset that communicates with the CPU, or host
processing unit 120, and assumes responsibility for accelerated
graphics port (AGP) communications. One or more graphics processing
units (GPUs) 184 may communicate with graphics interface 182. In
this regard, GPUs 184 generally include on-chip memory storage,
such as register storage and GPUs 184 communicate with a video
memory 186. GPUs 184, however, are but one example of a coprocessor
and thus a variety of coprocessing devices may be included in
computer 110. A monitor 191 or other type of display device is also
connected to the system bus 121 via an interface, such as a video
interface 190, which may in turn communicate with video memory 186.
In addition to monitor 191, computers may also include other
peripheral output devices such as speakers 197 and printer 196,
which may be connected through an output peripheral interface
195.
[0048] The computer 110 may operate in a networked or distributed
environment using logical connections to one or more remote
computers, such as a remote computer 180. The remote computer 180
may be a personal computer, a server, a router, a network PC, a
peer device or other common network node, and typically includes
many or all of the elements described above relative to the
computer 110, although only a memory storage device 181 has been
illustrated in FIG. 2B. The logical connections depicted in FIG. 2B
include a local area network (LAN) 171 and a wide area network
(WAN) 173, but may also include other networks/buses. Such
networking environments are commonplace in homes, offices,
enterprise-wide computer networks, intranets and the Internet.
[0049] When used in a LAN networking environment, the computer 110
is connected to the LAN 171 through a network interface or adapter
170. When used in a WAN networking environment, the computer 110
typically includes a modem 172 or other means for establishing
communications over the WAN 173, such as the Internet. The modem
172, which may be internal or external, may be connected to the
system bus 121 via the user input interface 160, or other
appropriate mechanism. In a networked environment, program modules
depicted relative to the computer 110, or portions thereof, may be
stored in the remote memory storage device. By way of example, and
not limitation, FIG. 2B illustrates remote application programs 185
as residing on memory device 181. It will be appreciated that the
network connections shown are exemplary and other means of
establishing a communications link between the computers may be
used.
[0050] Exemplary Distributed Computing Frameworks or
Architectures
[0051] Various distributed computing frameworks have been and are
being developed in light of the convergence of personal computing
and the Internet. Individuals and business users alike are provided
with a seamlessly interoperable and Web-enabled interface for
applications and computing devices, making computing activities
increasingly Web browser or network-oriented.
[0052] For example, MICROSOFT.RTM.'s .NET platform includes
servers, building-block services, such as Web-based data storage
and downloadable device software. Generally speaking, the .NET
platform provides (1) the ability to make the entire range of
computing devices work together and to have user information
automatically updated and synchronized on all of them, (2)
increased interactive capability for Web sites, enabled by greater
use of XML rather than HTML,(3) online services that feature
customized access and delivery of products and services to the user
from a central starting point for the management of various
applications, such as e-mail, for example, or software, such as
Office NET, (4) centralized data storage, which will increase
efficiency and ease of access to information, as well as
synchronization of information among users and devices, (5) the
ability to integrate various communications media, such as e-mail,
faxes, and telephones, (6) for developers, the ability to create
reusable modules, thereby increasing productivity and reducing the
number of programming errors and (7) many other cross-platform
integration features as well.
[0053] While exemplary embodiments herein are described in
connection with software residing on a computing device, one or
more portions of the invention may also be implemented via an
operating system, application programming interface (API) or a
"middle man" object between any of a coprocessor, a display device
and requesting object, such that prediction query generation and
execution services may be performed by, supported in or accessed
via all of .NET's languages and services, and in other distributed
computing frameworks as well.
[0054] Data Mining Prediction Query Building
[0055] The present invention thus provides systems and methods for
generating prediction queries to help a user build and execute
prediction queries. A UI is provided that is easy to use and
understand in connection with the generation of a prediction query
for data mining, and the UI can be instantiated from a variety of
disparate sources that may request query building services. In one
embodiment, users are able to build data mining prediction queries
in a way that is intuitively similar to building a join query for
data restricted to relational database(s).
[0056] As mentioned above, there exists a need in data mining
products for a tool that can assist a user in building data mining
prediction queries in a standard manner. In one aspect, the
prediction query builder of the invention allows a user to build
data mining queries in a manner similar to building/executing
relational join queries. The data mining model is treated like a
relational table and a prediction is treated like a join operation,
however, instead of joining two relational tables, the invention
enables the joining of a relational table with a mining model. At
least one difference between join operations in connection with a
relational query builder and join operations in connection with a
prediction query builder is that a relational query builder joins
the tables such that the data in each table can be related, e.g.,
records in table A and corresponding records in table B can be
related in some fashion depending on the join type whereas the
prediction query builder of the invention joins lines by mapping
columns from the source relational data to corresponding columns in
the mining model. In the relational case, both tables are input
data. In the prediction query builder case, mapping(s) are defined
from at least one source table to the mining model definition so
that when the query is executed, the mining model receives data fed
into the correct columns. Other differences may be evident from
further description herein. Towards the above goal(s), an improved
UI is provided in accordance with the invention, which permits a
user to specify simple two point "A-B" or "B-A" line segments to
mark the join condition and build the query.
[0057] The invention thus helps users to build data mining
prediction queries, which can otherwise become a complex
detail-oriented query drafting task. In one embodiment, the tool of
the invention is similar to some relational query builder
tools.
[0058] One of the open issues for data mining products is to allow
user to build a prediction query. As mentioned in the background,
most products do not include a query language for data mining. Some
other products do provide query languages for data mining
prediction; however, these languages are very different than
languages used for relational databases, and it is very difficult
to write without specific expertise to do so.
[0059] The invention thus proposes a way to help user to
build/execute data mining prediction queries in the same way as
building/executing relational join queries. A data mining model is
thus considered in a way that is similar to the way a relational
table is traditionally treated. A prediction query is thus
considered a join; however, instead of joining two relational
tables, a relational table is joined with a data mining model. A
user can delineate line segments by any input means, e.g., mouse,
keyboard, trackball, joystick, tablet pad, etc., to mark the join
condition. The following figures illustrate some of the main
concepts of the prediction query builder tool of the invention.
[0060] For instance, FIGS. 3A and 3B illustrate a prediction query
builder main screen. FIGS. 3A and 3B are the main query design
screen, with a first portion (the top part in this design) and a
second portion (the bottom part in this design). In the top part,
or first portion 300, there are two frames 302 and 310 with tree
views. The left one 302 is a mining model with mining model columns
shown by familiar file structure 304a. The predictable column 304b
(member_card in this case) has a diamond icon. Frame 302 includes a
select model link 306 to select a model in a familiar way. In other
embodiments of the invention, links, such as links 306, are
implemented as other kinds of UI elements, such as standard buttons
and the like. The right frame 310 is the schema of a relational
table. Frame 310 includes a modify join link 312, a delete table
link 314 and a select nested table link 316. The nested table link
316 enables a user to link tables having specific hierarchical and
nested relationships according to their relationships. A user can
then link columns between mining models 304b and input tables 318
by drawing line segments 319, using any conventional input means,
similar to the way a user builds a join query in a relational
system. The bottom part 320 is the selection grid. With the
selection grid, a user can select, e.g., "drag and drop," columns
from any of the top tree views to the grid, similar to the way a
user builds join query in a relational system. Also, similar to a
relational grid and described in more detail below, a user can
utilize dropdown controls, or other suitable controls, in the grid
to instead select columns of a mining model, various functions, or
define custom expressions in the grid. Exemplary embodiments of
grid 320 arc illustrated in FIGS. 9A and 9B below.
[0061] Contribution(s) of the invention, shown in FIGS. 3A and 3B,
include, but are not limited to, the display of data mining model
as a relational table, and the adoption of a query builder UI
familiar to users to build data mining prediction queries. No other
data mining products have thus far used these concepts.
[0062] FIG. 4 illustrates a prediction query builder SQL view 330
based on the query designed in FIG. 3A. In this particular
non-limiting example, the query syntax is based on object linking
and embedding database (OLE DB) for DM query language. This is
similar to a SQL view in a traditional relational query builder,
but instead is suited to prediction queries. One can see from this
example that the prediction query automatically generated by the
simple join operations of the invention is quite complex.
[0063] FIG. 5 is a prediction query builder grid view 340, which
displays the result(s) of the prediction query, after the
prediction query generated in FIG. 4 has been executed. This is
similar to the grid view shown as the result of a traditional join
operation in the relational database context. While not
illustrated, if the mining model contains nested tables, the result
grid 340 of the invention also supports hierarchical results.
[0064] Data mining prediction is an important step in data mining,
and thus providing a product having new functionality with a look
and feel that many users can already appreciate is an objective
achieved in accordance with the invention. The invention thus
adopts a classic relation query builder UI to help a user to
generate/execute prediction queries.
[0065] As mentioned above in connection with FIGS. 3A and 3B, the
main screen of the UI includes four main components: (1) a table
column mapping area, (2) a selection grid area, (3) a query text
display area and (4) a query result grid area. In one embodiment,
the query text display area and the query result grid area are
initially invisible.
[0066] The invention will now be described with reference to
various more detailed, but nonlimiting embodiments. In connection
with the table column mapping area 300, at the initial stage, as
shown in FIG. 6, the user specifies the mining model based upon
which prediction is going to take place by selecting link 306.
[0067] The select mining model list box 700 shown in FIG. 7A
displays the list of mining models in the solution. It is possible
for the user to select a mining model within the project, or refer
to an existing model from a server. In one embodiment, the default
mining model is the first one in the list of derived mining
models.
[0068] Once the model is selected, the hyper link 316 to "Select
one or more input tables" becomes enabled. The user can follow the
hyper link 316 to select the input tables based on the mining model
structure selected. Thus, once the mining model is selected, the
user can pick input tables for prediction (the `Select tables` link
316 is enabled). While clicking on the link, the user is prompted
if he wants to select the table from existing Data Source Views
(DSVs) or from a live server.
[0069] FIG. 7B illustrates an exemplary "Selecting case table or
nested tables for prediction" UI object 710, enabling a user to
select input source objects. Then, as illustrated in FIG. 7C, if
there are nested tables, the user draws the mapping 720 between the
case table and any nested tables.
[0070] As illustrated in FIG. 8, when the table selection wizard is
done, the user is returned to the prediction query builder main
screen with the columns of the input table populated as per the
selections of FIGS. 7B and 7C. Columns from model and tables, which
have same name, are then automatically mapped.
[0071] FIGS. 9A and 9B illustrate exemplary implementations for a
grid 320a or grid 320b, on which a user can select, e.g.,
drag-drop, columns from mining model columns, input table columns
and available prediction functions. The source column 321 allows a
user to select columns from the mining model, input tables,
prediction functions or user expression. The Field column 322
allows the user to pick the columns or prediction functions. The
alias column 323 gives the user the option to rename the display
name of a column in the result grid view. The show column 324 gives
the user the option to show a source in the grid 320a or 320b. The
Group column 325 allows the user to group Boolean expressions
together while the And/Or column 326 allows a user to specify the
expression. The user can type the condition or user expression in
the criteria column 327.
[0072] FIG. 10 illustrates another exemplary prediction query 1000
generated in accordance with the invention showing how complicated
such a query can be. The simple selection and mapping/join
operation of the invention generates such complex queries
automatically and thus is of great utility from the standpoint of
preventing mistakes, and saving time in learning and programming
prediction query syntax. In one embodiment, via any common input
means, e.g., keyboard and the like, a user can manually edit text
generated by the builder, e.g., SQL syntax, and save the
modifications. This functionality is optionally provided for
convenience, and, in one implementation, the builder does not load
UI state from the query text, i.e., the UI does not reflect changes
to the query text.
[0073] Exemplary items in the toolbar for the UI of the invention
include: (1) Save, which saves a query, (2) Open, which opens a
query, (3) Run, which executes the query, Show syntax, which
displays the query syntax in a window and (5) Design/Grid Viewer
toggle, which switches back to the design view or to the query
result view.
[0074] In one embodiment, the invention provides support for a
singleton query, wherein a choice is added at the bottom of the
Input Table Grid, which invokes a hierarchical grid with two
columns: Attribute and Value. The user can type the value for some
attributes and when returned to the main screen, the columns
associated with the values are automatically mapped.
CONCLUSION
[0075] The Data Mining (DM) prediction query builder, or generator,
of the invention is a data mining tool that helps a user to build
and execute prediction queries. In this regard, the present
invention provides a user interface (UI) that is easy to use and
understand in connection with the generation of a query for data
mining, and can be instantiated from a variety of disparate sources
that may request query building services. It is noted that a
relational query and a prediction query are unrelated in purpose
and effect; however, in one aspect, the UI of this invention
includes a "feel and effect" similar to a relational query building
model.
[0076] As mentioned above, while exemplary embodiments of the
present invention have been described in connection with various
computing devices and network architectures, the underlying
concepts may be applied to any computing device or system in which
it is desirable to generate and execute prediction queries. Thus,
the techniques for providing prediction query generation and
execution in accordance with the present invention may be applied
to a variety of applications and devices. For instance, the
algorithm(s) of the invention may be applied to the operating
system of a computing device, provided as a separate object on the
device, as part of another object, as a downloadable object from a
server, as a "middle man" between a device or object and the
network, as a distributed object, etc. While exemplary programming
languages, names and examples are chosen herein as representative
of various choices, these languages, names and examples are not
intended to be limiting. One of ordinary skill in the art will
appreciate that there are numerous ways of providing object code
that achieves the same, similar or equivalent prediction query
generation and execution achieved by the invention.
[0077] The various techniques described herein may be implemented
in connection with hardware or software or, where appropriate, with
a combination of both. Thus, the methods and apparatus of the
present invention, or certain aspects or portions thereof, may take
the form of program code (i.e., instructions) embodied in tangible
media, such as floppy diskettes, CD-ROMs, hard drives, or any other
machine-readable storage medium, wherein, when the program code is
loaded into and executed by a machine, such as a computer, the
machine becomes an apparatus for practicing the invention. In the
case of program code execution on programmable computers, the
computing device will generally include a processor, a storage
medium readable by the processor (including volatile and
non-volatile memory and/or storage elements), at least one input
device, and at least one output device. One or more programs that
may utilize the signal processing services of the present
invention, e.g., through the use of a data processing API or the
like, are preferably implemented in a high level procedural or
object oriented programming language to communicate with a computer
system. However, the program(s) can be implemented in assembly or
machine language, if desired. In any case, the language may be a
compiled or interpreted language, and combined with hardware
implementations.
[0078] The methods and apparatus of the present invention may also
be practiced via communications embodied in the form of program
code that is transmitted over some transmission medium, such as
over electrical wiring or cabling, through fiber optics, or via any
other form of transmission, wherein, when the program code is
received and loaded into and executed by a machine, such as an
EPROM, a gate array, a programmable logic device (PLD), a client
computer, a video recorder or the like, or a receiving machine
having the signal processing capabilities as described in exemplary
embodiments above becomes an apparatus for practicing the
invention. When implemented on a general-purpose processor, the
program code combines with the processor to provide a unique
apparatus that operates to invoke the functionality of the present
invention. Additionally, any storage techniques used in connection
with the present invention may invariably be a combination of
hardware and software.
[0079] While the present invention has been described in connection
with the preferred embodiments of the various figures, it is to be
understood that other similar embodiments may be used or
modifications and additions may be made to the described embodiment
for performing the same function of the present invention without
deviating therefrom. For example, while exemplary network
environments of the invention are described in the context of a
networked environment, such as a peer to peer networked
environment, one skilled in the art will recognize that the present
invention is not limited thereto, and that the methods, as
described in the present application may apply to any computing
device or environment, such as a gaming console, handheld computer,
portable computer, etc., whether wired or wireless, and may be
applied to any number of such computing devices connected via a
communications network, and interacting across the network.
Furthermore, it should be emphasized that a variety of computer
platforms, including handheld device operating systems and other
application specific operating systems are contemplated, especially
as the number of wireless networked devices continues to
proliferate. Still further, the present invention may be
implemented in or across a plurality of processing chips or
devices, and storage may similarly be effected across a plurality
of devices. Therefore, the present invention should not be limited
to any single embodiment, but rather should be construed in breadth
and scope in accordance with the appended claims.
* * * * *